1. topic-models
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1.1. stand-alone applications
- TOME (website repository )
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'TOME is a tool to support the interactive exploration and visualization of text-based archives, supported by a Digital Humanities Startup Grant from the National Endowment for the Humanities (Lauren Klein and Jacob Eisenstein, co-PIs). Drawing upon the technique of topic modeling—a machine learning method for identifying the set of topics, or themes, in a document set—our tool allows humanities scholars to trace the evolution and circulation of these themes across networks and over time.' < | Unknown | stand-alone application | Python, Jupyter Notebook | >
1.2. programming-frameworks/libraries etc.
1.2.1. R
- Stm (website repository )
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'The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et al (2014) <doi:10.1111/ajps.12103> and Roberts et al (2016) <doi:10.1080/01621459.2016.1141684>.' < | MIT | library | R | >
1.2.2. Others
- MALLET (website repository )
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< | Apache-2.0 | library | Java | >